Read the equation
The update stores an increment equal to 0.02 times the sum of normalized input and utility. Repeated updates accumulate their history in a single scalar, so different histories with the same accumulated sum are indistinguishable. No forgetting factor appears in this individual rule.
Terms & definitions
| Term | Meaning in this source |
|---|---|
| x | Accumulated trace |
| dx_norm | Input contribution |
| utility | Utility contribution |
How it works
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Assumptions & limits
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Original context
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## 3.14 Family 13: Memory Trace (Zecher) ``` f₁₃(x) = x + 0.02 · (dx_norm + utility) ``` **Equation Type**: Trace accumulation: x'_i = Σ f_i : dΩ **Explanation**: Slowly accumulates both change and utility signals, creating persistent memory traces. Named "Zecher" (Hebrew for "memory"). ## 3.15 Family 14: Decisive Action / Policy ``` f₁₄(x) = x + 0.15 · softsign(utility) - 0.05 · ed_error ``` **Equation Type**: Policy gradient: π(a|s) **Explanation**: Generates action outputs by amplifying utility signals and suppressing errors. The larger positive coefficient on utility makes this an action-oriented family. ## 3.16 Family 15: Global Coherence (Kehillah) ``` f₁₅(x) = x + (φ - 1) · 0.01 · (stability - x)
Exact original notation
f₁₃(x) = x + 0.02 · (dx_norm + utility)